title: Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers.
abstract: BACKGROUND
  Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer.
  
  
  RESULTS
  We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis.
  
  
  CONCLUSIONS
  In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/.
authors.affiliation: Masonic Cancer Center, University of Minnesota - Twin Cities, Minneapolis, MN, USA. taehyun.hwang@utsouthwestern.edu
authors.forename: Tae Hyun
authors.initials: TH
authors.lastname: Hwang
authors.forename: Gowtham
authors.initials: G
authors.lastname: Atluri
authors.forename: Rui
authors.initials: R
authors.lastname: Kuang
authors.forename: Vipin
authors.initials: V
authors.lastname: Kumar
authors.forename: Timothy
authors.initials: T
authors.lastname: Starr
authors.forename: Kevin At
authors.initials: KA
authors.lastname: Silverstein
authors.forename: Peter M
authors.initials: PM
authors.lastname: Haverty
authors.forename: Zemin
authors.initials: Z
authors.lastname: Zhang
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Liu
mesh_terms: D056915:DNA Copy Number Variations; D030541:Databases, Genetic; D023281:Genomics; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism* / Q000473:pathology; D060066:Protein Interaction Maps; D015398:Signal Transduction / Q000235:genetics; D016019:Survival Analysis; D049490:Systems Biology / Q000379:methods*; D016212:Transforming Growth Factor beta / Q000378:metabolism
chemical_list: D016212:Transforming Growth Factor beta